In light of the extreme mortality rates associated with human infection with avian influenza strains, the emergence of a pandemic influenza outbreak is of immense concern. Pandemic strains are thought to occur through the introduction of genetically divergent protein sequences by reassortment of viral genomic segments during coinfection with multiple influenza viruses (antigenic shift), as well as gradual accumulation of mutations sufficient to alter the antigenicity of a given strain such that preexisting host immunity becomes ineffective (antigenic drift). The goal of this project is to mirror these processes in vitro by using an unbiased combinatorial library approach, thereby allowing access to large sections of antigenic space. Specifically, we will utilize phage escape technologies developed in our laboratory to generate combinatorial libraries of the viral surface glycoprotein hemagglutinin (HA) to allow access to the sequence space available to the influenza virus. The essence of our tactic is an alternating scheme of screening for human antibodies which first prevent HA binding to cells, followed by a selection for mutant HA proteins that "escape" our antibodies and remain bound to cells. Repeated iteration of this process will reveal an array of varying HA clones capable of binding to cells, as well as the set of neutralizing antibodies corresponding to each HA. Ultimately, we envision that our approach will allow for an assessment of both the current avian influenza strain (H5), virulent HA mutants which occurred in the past (e.g., H1), as well as those that have not yet emerged. Furthermore, inherent to our experimental design is a concurrent identification of specific human antibodies that neutralize HA binding to host cells. PUBLIC HEALTH RELEVANCE: Rarely does a year go by that influenza is not a public health concern, and in particular, the threat of a pandemic avian influenza in recent years has caused immense anxiety in the minds of the public. Our proposal utilizes modern molecular biology techniques to mimic the evolution of the influenza protein required for infection. We aim to generate a laboratory system that can predict influenza strains and in the process, develop neutralizing therapies for these strains before they can become a threat to public health.